* Feature: match fuzzy terms in place inside the parsed query
Fuzzy matching was a separate clause OR'd in above the query: a flat bag
of the query's words, re-parsed through tantivy's own parser, blended
beside the exact clause. Nothing around a term reached it, so a fielded
term fuzzed across every default field, a filter did not constrain it, and
an exclusion had to be hoisted back over the whole blend to stop the
clause re-admitting what the query had just excluded.
Widen each leaf where it sits instead, through emit()'s rewrite_leaf hook,
so fielding, negation, AND, REQUIRE and positive filters constrain the
fuzzy match exactly as they constrain the exact one. Each of a leaf's
words becomes a Fuzzy leaf on the leaf's own field, boosted to 0.1, beside
the leaf and any CJK alternative it already had.
* Hello?
QUERY-mode searches blended a separate bigram clause in at the top of the
query, built from the parsed AST's free-text tokens. Because it sat beside
the exact clause rather than inside the query, nothing around a CJK term
constrained its bigram match: an exclusion that was one OR branch's own
condition could never reach it, so "(東京 AND NOT secret) OR bill" still
returned the secret document.
Widen each CJK leaf where it sits instead, through emit()'s rewrite_leaf
hook, so every AND, NOT, REQUIRE, boost and field restriction around the
leaf applies to its bigram match too. Negated leaves are widened on
purpose, so "NOT X" excludes exactly what "X" matches.
* Preprocesses classifier content with Tantivy instead of NLTK
Tokenizing and stemming now happen in one Rust call instead of NLTK's
Python tokenizer and per word stemming, which also removes the Redis
backed stem cache from every preprocessing call. The output matches the
NLTK pipeline closely; tokens containing digits are now stemmed, and the
English stop words follow Snowball's list.
Stemming and stop word removal apply whenever the OCR language is one of
the supported classifier languages, so PAPERLESS_ENABLE_NLTK and
PAPERLESS_NLTK_DIR are removed.
* Copies packages instead of hardlinking them in backend CI, some NLTK thing
* Adds a normalization to NFC to better fit what Tantivy expects
BulkEditSerializer's hand-parsed validators only caught the exceptions
their happy paths raised, so wrong-typed input produced a 500 or was
passed through to the task:
- owner: a nonexistent or wrong-typed id raised an uncaught error, and a
boolean was accepted (Django coerces True to pk 1). It is now validated
with PrimaryKeyRelatedField and the validated pk is passed on.
- rotate: null raised TypeError, while true, "90" and 45 were accepted and
failed later in QPDF. Degrees are now an IntegerField plus a
multiple-of-90 check, passing an int on. The dedicated rotate endpoint
and edit_pdf operations share the same multiple-of-90 check.
- split: null raised AttributeError, "0" silently became the last page,
"3-1" gave an empty group that crashed the task, and a range like
"1-5000000" was expanded into a list during the request with no upper
bound. Each range is now checked against 1 <= start <= end <= page_count
before it is built.
SetPermissionsSerializer was a bare DictField, and the legacy bulk_edit
set_permissions parameter bypassed even that by hand-calling
validate_set_permissions() on an unchecked dict. A bool or a non-list in
place of users/groups crashed with a raw TypeError instead of a 400.
It is now a nested serializer (view/change, each with users/groups as
lists of integers), used for owned-object create/update, the legacy
bulk_edit set_permissions parameter, and bulk_edit_objects permissions.
Unknown action keys are rejected: previously a typo like "veiw" was
silently dropped, leaving an empty permission set that could clear
existing grants. An explicit set_permissions null (an owner-only change)
remains a no-op, and an empty bulk_edit_objects permissions dict is still
rejected.
JSONField(binary=True) accepts any JSON value, so a truthy non-dict
(bool/int/list/string) silently passed validation.
Additionally, require string substitutes in barcode_tag_mapping, a non-string
substitute value made the barcode tag substitution fail inside
get_tag_ids, and the tag was silently skipped. barcode_tag_mapping values
must now be strings (empty still means "use the raw barcode").
operations was a ListField with no child, so both EditPdfDocumentsSerializer
and BulkEditSerializer._validate_parameters_edit_pdf hand-checked each entry
with isinstance(). That accepted booleans (isinstance(True, int) is true) and
passed through any extra keys.
Adds PdfEditOperationSerializer (page >= 1, doc >= 0, rotate a multiple of
90) and uses it as the ListField child on both paths, dropping the manual
type checks and the now-redundant negative/page < 1 bounds checks. Rotations
that are not a multiple of 90 previously passed validation and then failed
inside the task when QPDF refused them.
* docs(search): document the query grammar paperless actually supports
* docs(search): cover ?/bracket wildcards and the no-stemmer fallback
* Shorten things up, use tables and less wordy stuff. Fixes some test doc strings that referred to the documentation
* Silly sonarr
* Feature: parse advanced search with whoosh-compat and delete the hand-written translator
* Don't cover these, they exist for defensive, but there's no other current diagnostic
* A mix of more no cover and tests
* Route SCHEMA_FIELD_MISSING into an error, not an HTTP 400
* feat(search): add whoosh-compat, the shared field table and the field registry
* Sonar being useful actually
* Adds the given/when/then commenting
* Trims tests I don't think cover our logic or code or are redundant
* Coverage
* test(search): add pattern normalizer stem-alternates unit tests
* build: bump whoosh-compat to 0.2.0
shamoon
Trenton H
github-actions[bot]andCrowdin Bot
Boris Rybalkin